1. Field of the Invention
This invention relates to the control of metal rolling mills. More particularly this invention relates to the use of an adaptive artificial neural network in a control loop for regulating the final gauge of a metal sheet produced by a metal rolling mill.
2. Description of the Prior Art
An integrated rolling mill passes a preprocessed metal casting through a succession of rolling stages to yield a homogeneous metal plate. This invention is directed to the control of the thickness of the metal in a rolling mill.
Control of the output of a metal rolling mill to achieve a product having a uniform gauge is a complex problem. Rolling mills are an example of complex industrial systems whose outputs are affected by a multiplicity of non-linear, time-varying states that are mutually coupled in an uncertain manner. For example it is known that the yield stress of the metal is a non-linear function of the strain rate, temperature, and the passage velocity. Internal states in the mill itself also affect the rolling force at any given instant. Prediction of the behavior of a rolling mill defies closed solution.
Proportional integral-derivative controllers (PID controllers) are commonly used in the art to control the rolling force of the mill in order to achieve exit gauge uniformity within a target range. This control method is limited by transportation delays of the metal from the mill rolls to the gauge sensors, which are necessarily spaced apart from the rolls. The method is further limited by the inherent delay in the operation of the gauge sensors, by sparsity of measurement data, and by high frequency noise and other systematic error in the measurements. The achievable target range is relatively large with this technique.
More sophisticated predictive approaches to rolling mill operation such as rule-based expert systems and highly parameterized analytic models have been attempted. More recently artificial neural networks have been introduced to solve certain problems of rolling mill operation.
For example in Ah Chung Tsoi, Advances in Neural Information Processing (vol. 4), Houson and Lippman, Eds., Morgan Kaufman, 1992, pp. 698-705 there is disclosed an empirically derived mathematical formula for predicting yield stress EQU k.sub.m =a.epsilon..sup.b sin h.sup.-1 (c.epsilon. exp(d/T).sup.f)
where k.sub.m is the yield stress, .epsilon. is the strain, .epsilon. is the corresponding strain rate, T is temperature, and a, b, c, d, and f are unknown constants. Tsoi further suggests that an artificial neural network employing an additive nonlinear model and using the independent variables of the above equation can accept a training data set taken from previous mill runs to predict the actual output of the plate mill with a smaller error than the above equation.
Lu et al, U.S. Pat. No. 5,159,660, discloses an adaptive control system for a complex process that incorporates an artificial neural network. The inputs to the artificial neural network are a time sequence of error values, and the neuron paths are weighted as a function of these error values and also of the process output. While this technique is suitable for certain kinds of processes having nonlinear time-varying behavior, it has the same limitation of other prior art attempts to control a rolling mill, namely the delay between the present states of the mill and the measurement of the mill's output.
Another application of an artificial neural network to a somewhat different problem is disclosed in Roscheisen et al, Advances in Neural Information Processing (vol. 4), Houson and Lippman, Eds., Morgan Kaufman, 1992, p. 659, there is disclosed a solution to the control problem of determining a reduction schedule for the mill. Neural nets having specialized architectures and utilizing a Bayesian framework were trained to represent an instantiation of a large parameterized analytic model for this problem. The operations of the nets were then cross-validated with the predictions of the analytic model, particularly in regions of an input hyperspace where no data were available. While this publication is of theoretical interest, it does not explain how an artificial neural network could actually be implemented into the control devices of an operating rolling mill.
More recently Sbarbaro-Hofer et al, IEEE Control Systems, June 1993, pp 69-75 address the problem of neural control of a steel rolling mill. In this document an internal model control scheme and a predictive control model scheme are proposed. Both schemes use simplified mathematical assumptions for the development of training data for the artificial neural network. In the internal model scheme, the network is employed in a closed loop control arrangement with incorporation of suitable filters and time delays. In the predictive model, discrete dynamic optimization of the model is undertaken. In both cases, feedback to the network is provided by coupling an input of the network to the exit gauge sensor, and the network is thus dynamically adapted. However the dynamic response of the network is irreducibly delayed by the dead time measured from the metal passing between the rollers and the output of the exit gauge. The authors essentially approach a complex nonlinear control problem by utilizing an artificial neural network to directly modify the output of the PID controller, or to replace the controller entirely. The models utilized by the authors ignore noise effects, as well as the dynamic effects of the sensors and actuators. While ignoring such effects may be useful for theoretical study, it is a luxury that cannot be indulged in controlling a practical rolling mill.